U.S. patent number 9,787,841 [Application Number 14/956,074] was granted by the patent office on 2017-10-10 for techniques for hybrid behavioral pairing in a contact center system.
This patent grant is currently assigned to Afiniti Europe Technologies Limited. The grantee listed for this patent is Afiniti Europe Technologies Limited. Invention is credited to Zia Chishti, Vikash Khatri.
United States Patent |
9,787,841 |
Chishti , et al. |
October 10, 2017 |
Techniques for hybrid behavioral pairing in a contact center
system
Abstract
Techniques for hybrid behavioral pairing in a contact center
system are disclosed. In one particular embodiment, the techniques
may be realized as a method for hybrid behavioral pairing in a
contact center system comprising: ordering an agent; ordering a
plurality of contacts; applying, by at least one processor, a
hybridization function to the ordering of the plurality of contacts
to bias a first strategy for pairing toward a second strategy for
pairing; comparing, by the at least one processor and based on the
hybridization function, a first difference in ordering between the
agent and a first contact in a first pair with a second difference
in ordering between the agent and a second contact different from
the first contact in a second pair; and selecting, by the at least
one processor, the first pair or the second pair for connection
based on the comparing.
Inventors: |
Chishti; Zia (Washington,
DC), Khatri; Vikash (Arlington, VA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Afiniti Europe Technologies Limited |
Cheshire |
N/A |
GB |
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Assignee: |
Afiniti Europe Technologies
Limited (Cheshire, GB)
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Family
ID: |
58104461 |
Appl.
No.: |
14/956,074 |
Filed: |
December 1, 2015 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20170064080 A1 |
Mar 2, 2017 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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14871658 |
Sep 30, 2015 |
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14530058 |
Oct 31, 2014 |
9277055 |
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13843724 |
Nov 4, 2014 |
8879715 |
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12021251 |
Jan 28, 2008 |
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61615772 |
Mar 26, 2012 |
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61615788 |
Mar 26, 2012 |
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61615779 |
Mar 26, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04M
3/5233 (20130101); H04M 3/5234 (20130101); H04M
3/5232 (20130101); H04M 2201/36 (20130101); H04M
3/5235 (20130101); H04M 2201/18 (20130101) |
Current International
Class: |
H04M
3/00 (20060101); H04M 3/523 (20060101); H04M
5/00 (20060101) |
Field of
Search: |
;379/265.01,266.01,309
;705/7.13 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
2008349500 |
|
May 2014 |
|
AU |
|
2009209317 |
|
May 2014 |
|
AU |
|
2009311534 |
|
Aug 2014 |
|
AU |
|
102301688 |
|
May 2014 |
|
CN |
|
102017591 |
|
Nov 2014 |
|
CN |
|
0 493 292 |
|
Jul 1992 |
|
EP |
|
0 949 793 |
|
Oct 1999 |
|
EP |
|
1 032 188 |
|
Aug 2000 |
|
EP |
|
1335572 |
|
Aug 2003 |
|
EP |
|
11-098252 |
|
Apr 1999 |
|
JP |
|
2000-078291 |
|
Mar 2000 |
|
JP |
|
2000-078292 |
|
Mar 2000 |
|
JP |
|
2000-092213 |
|
Mar 2000 |
|
JP |
|
2000-236393 |
|
Aug 2000 |
|
JP |
|
2001-292236 |
|
Oct 2001 |
|
JP |
|
2001-518753 |
|
Oct 2001 |
|
JP |
|
2002-297900 |
|
Oct 2002 |
|
JP |
|
3366565 |
|
Jan 2003 |
|
JP |
|
2003-187061 |
|
Jul 2003 |
|
JP |
|
2004-056517 |
|
Feb 2004 |
|
JP |
|
2004-227228 |
|
Aug 2004 |
|
JP |
|
2006-345132 |
|
Dec 2006 |
|
JP |
|
2007-324708 |
|
Dec 2007 |
|
JP |
|
2011-511533 |
|
Apr 2011 |
|
JP |
|
2011-511536 |
|
Apr 2011 |
|
JP |
|
5421928 |
|
Feb 2014 |
|
JP |
|
5631326 |
|
Nov 2014 |
|
JP |
|
5649575 |
|
Jan 2015 |
|
JP |
|
316118 |
|
Dec 2013 |
|
MX |
|
322251 |
|
Jul 2014 |
|
MX |
|
587100 |
|
Oct 2013 |
|
NZ |
|
587101 |
|
Oct 2013 |
|
NZ |
|
591486 |
|
Jan 2014 |
|
NZ |
|
592781 |
|
Mar 2014 |
|
NZ |
|
1-2010-501704 |
|
Feb 2014 |
|
PH |
|
1-2010-501705 |
|
Feb 2015 |
|
PH |
|
WO-99/17517 |
|
Apr 1999 |
|
WO |
|
WO-01/63894 |
|
Aug 2001 |
|
WO |
|
WO-2006/124113 |
|
Nov 2006 |
|
WO |
|
WO-2010/053701 |
|
May 2010 |
|
WO |
|
WO-2011/081514 |
|
Jul 2011 |
|
WO |
|
Other References
Anonymous. (2006) "Performance Based Routing in Profit Call
Centers," The Decision Makers' Direct, located at
www.decisioncraft.com, Issue Jan. 6, 2012 (3 pages). cited by
applicant .
Cleveland, William S., "Robust Locally Weighted Regression and
Smoothing Scatterplots," Journal of the American Statistical
Association, vol. 74, No. 368, pp. 829-836 (Dec. 1979). cited by
applicant .
Gans, N. et al. (2003), "Telephone Call Centers: Tutorial, Review
and Research Prospects," Manufacturing & Service Operations
Management, vol. 5, No. 2, pp. 79-141. cited by applicant .
International Preliminary Report on Patentability issued in
connection with PCT Application No. PCT/US2009/066254 dated Jun.
14, 2011 (6 pages). cited by applicant .
International Search Report issued in connection with International
Application No. PCT/US13/33268 dated May 31, 2013 (2 pages). cited
by applicant .
International Search Report issued in connection with PCT
Application No. PCT/US/2009/054352 dated Mar. 12, 2010, 5 pages.
cited by applicant .
International Search Report issued in connection with PCT
Application No. PCT/US2008/077042 dated Mar. 13, 2009 (3 pages).
cited by applicant .
International Search Report issued in connection with PCT
Application No. PCT/US2009/031611 dated Jun. 3, 2009 (5 pages).
cited by applicant .
International Search Report issued in connection with PCT
Application No. PCT/US2009/066254 dated Feb. 24, 2010 (4 pages).
cited by applicant .
International Search Report issued in connection with
PCT/US2009/061537 dated Jun. 7, 2010 (5 pages). cited by applicant
.
International Search Report issued in connection with
PCT/US2013/033261 dated Jun. 14, 2013 (3 pages). cited by applicant
.
International Search Report issued in connection with
PCT/US2013/33265 dated Jul. 9, 2013 (2 pages). cited by applicant
.
Koole, G. (2004). "Performance Analysis and Optimization in
Customer Contact Centers," Proceedings of the Quantitative
Evaluation of Systems, First International Conference, Sep. 27-30,
2004 (4 pages). cited by applicant .
Koole, G. et al. (Mar. 6, 2006). "An Overview of Routing and
Staffing Algorithms in Multi-Skill Customer Contact Centers,"
Manuscript, 42 pages. cited by applicant .
Ntzoufras, "Bayesian Modeling Using Winbugs". Wiley Interscience,
Chapter 5, Normal Regression Models, Oct. 18, 2007, pp. 155-220 (67
pages). cited by applicant .
Press, W. H. and Rybicki, G. B., "Fast Algorithm for Spectral
Analysis of Unevenly Sampled Data," The Astrophysical Journal, vol.
338, pp. 277-280 (Mar. 1, 1989). cited by applicant .
Riedmiller, M. et al. (1993). "A Direct Adaptive Method for Faster
Back Propagation Learning: The RPROP Algorithm," 1993 IEEE
International Conference on Neural Networks, San Francisco, CA,
Mar. 28-Apr. 1, 1993, 1:586-591. cited by applicant .
Stanley et al., "Improving call center operations using
performance-based routing strategies," Calif. Journal of Operations
Management, 6(1), 24-32, Feb. 2008; retrieved from
http://userwww.sfsu.edu/saltzman/Publist.html. cited by applicant
.
Subsequent Substantive Examination Report issued in connection with
Philippines Application No. 1-2010-501705 dated Jul. 14, 2014 (1
page). cited by applicant .
Substantive Examination Report issued in connection with
Philippines Application No. 1/2011/500868 dated May 2, 2014 (1
page). cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT Application No. PCT/US2008/077042 dated Mar.
13, 2009, 6 pages. cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with International Application No. PCT/US13/33268 dated
May 31, 2013, 7 pages. cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT Application No. PCT/US/2009/054352 dated Mar.
12, 2010, 5 pages. cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT Application No. PCT/US2009/031611 dated Jun. 3,
2009, 7 pages. cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT Application No. PCT/US2009/066254 dated Feb. 4,
2010, 5 pages. cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT/US2009/061537 dated Jun. 7, 2010, 10 pages.
cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT/US2013/033261 dated Jun. 14, 2013, 7 pages.
cited by applicant .
Written Opinion of the International Searching Authority issued in
connection with PCT/US2013/33265 dated Jul. 9, 2013, 7 pages. cited
by applicant .
Japanese Office Action issued by the Japan Patent Office for
Application No. 2015-503396 dated Jun. 29, 2016 (7 pages). cited by
applicant .
Canadian Office Action issued in Canadian Patent Application No.
2713526, dated Oct. 25, 2016, 7 pages. cited by applicant .
Extended European Search Report issued by the European Patent
Office for European Application No. 17154781.3 dated May 4, 2017 (7
pages). cited by applicant .
International Search Report and Written Opinion issued by the
European Patent Office as International Searching Authority for
International Application No. PCT/IB2016/001762 dated Feb. 20, 2017
(15 pages). cited by applicant .
International Search Report and Written Opinion issued by the
European Patent Office as International Searching Authority for
International Application No. PCT/IB2016/001776 dated Mar. 3, 2017
(16 pages). cited by applicant .
International Search Report and Written Opinion issued by the
European Patent Office as International Searching Authority for
International Application No. PCT/162017/000570 dated Jun. 30, 2017
(13 pages). cited by applicant.
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Primary Examiner: Nguyen; Quynh
Attorney, Agent or Firm: Wilmer Cutler Pickering Hale and
Dorr LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent
application Ser. No. 14/871,658, filed Sep. 30, 2015, which is a
continuation-in-part of U.S. patent application Ser. No.
12/021,251, filed Jan. 28, 2008, and is a continuation-in-part of
U.S. patent application Ser. No. 14/530,058, filed Oct. 31, 2014,
which is a continuation of U.S. patent application Ser. No.
13/843,724, filed Mar. 15, 2013, now U.S. Pat. No. 8,879,715,
issued Nov. 4, 2014, which claims priority to U.S. Provisional
Patent Application No. 61/615,788, filed Mar. 26, 2012, U.S.
Provisional Patent Application No. 61/615,779, filed Mar. 26, 2012,
and U.S. Provisional Patent Application No. 61/615,772, filed Mar.
26, 2012, each of which is hereby incorporated by reference in
their entirety as if fully set forth herein.
This application is also related to U.S. patent application Ser.
No. 14/956,086, entitled "Techniques for Hybrid Behavioral Pairing
in a Contact Center System," filed Dec. 1, 2015.
Claims
The invention claimed is:
1. A method for hybrid behavioral pairing in a contact center
system comprising: determining, by at least one computer processor
communicatively coupled to and configured to operate in the contact
center system, an ordering of an available agent of a plurality of
agents in the contact center system; ordering, by the at least one
computer processor, a plurality of contacts; adjusting, by the at
least one computer processor, the ordering of the plurality of
contacts to bias a first strategy for pairing with a balanced
contact utilization toward a second strategy for pairing with a
skewed contact utilization, wherein an extent of the adjusting is
based at least in part on a hybridization function; comparing, by
the at least one computer processor and after the adjusting, a
first difference in ordering between the available agent and a
first contact of the plurality of contacts as adjusted in a first
pair with a second difference in ordering between the available
agent and a second contact of the plurality of contacts as adjusted
different from the first contact in a second pair; selecting, by
the at least one computer processor, one of the first pair and the
second pair for connection based at least in part upon the
comparing; and outputting, by the at least one computer processor,
the selection of one of the first pair and the second pair, wherein
the selected one of the first pair and the second pair is connected
in the contact center system based at least in part upon the
outputting.
2. The method of claim 1, wherein selecting the first pair or the
second pair based on the comparing further comprises applying, by
the at least one computer processor, a diagonal strategy to the
orderings.
3. The method of claim 1, wherein determining the ordering of the
available agent or the ordering of the plurality of contacts is
expressed as percentiles.
4. The method of claim 1, wherein determining the ordering of the
available agent or the ordering of the plurality of contacts is
expressed as percentile ranges.
5. The method of claim 4, wherein each of the plurality of contacts
is assigned a percentile within each contact's respective
percentile range.
6. The method of claim 5, wherein an assigned percentile is a
midpoint of a percentile range.
7. The method of claim 5, wherein an assigned percentile is a
random percentile of a percentile range.
8. The method of claim 1, further comprising determining, by the at
least one computer processor, a bandwidth for the agent
proportionate to a relative performance of the agent.
9. The method of claim 1, wherein the hybridization function
enables controllably targeting, by the at least one computer
processor, an unbalanced contact utilization.
10. The method of claim 9, wherein the hybridization function
comprises determining, by the at least one computer processor,
disproportional bandwidth for each of a plurality of contact
types.
11. The method of claim 1, wherein a selected contact or contact
type of the selected pair is not any of: a contact or contact type
lagging in a fairness metric, a contact or contact type rated
highest in a value, priority, or performance metric, a contact or
contact type rated highest in a value, priority, or performance
metric for a particular agent, a contact previously assigned to the
agent of the selected pair, a sequentially labeled contact or
contact type, or a randomly selected contact or contact type.
12. The method of claim 1, wherein the selected one of the first
pair and the second pair comprises a worse expected instant outcome
than the other of the first pair and the second pair.
13. The method of claim 1, wherein a higher-ordered agent remains
available for subsequent assignment to a similarly higher-ordered
contact, or a higher-ordered contact remains available for
subsequent assignment to a similarly higher-ordered agent.
14. The method of claim 1, wherein the first strategy comprises a
behavioral pairing strategy, and wherein the second strategy
comprises a priority queuing strategy.
15. The method of claim 1, wherein each successively higher-ordered
contact of the plurality of contacts is more likely to be selected
than respectively lower-ordered contacts.
16. The method of claim 1, wherein each successively higher-ordered
contact of the plurality of contacts comprises a lower average
waiting time than respectively lower-ordered contacts.
17. A method for hybrid behavioral pairing in a contact center
system comprising: determining, by at least one computer processor
communicatively coupled to and configured to operate in the contact
center system, an ordering of an available agent of a plurality of
agents in the contact center system; determining, by the at least
one computer processor, a first ordering of a plurality of contacts
according to a first strategy for pairing with a balanced contact
utilization; determining, by the at least one computer processor, a
second ordering of the plurality of contacts according to a second
strategy for pairing with a skewed contact utilization; adjusting,
by the at least one computer processor, the first ordering
according to the second ordering, wherein an extent of the
adjusting is based at least in part on a hybridization function;
comparing, by the at least one computer processor and after the
adjusting, a first difference in ordering between the available
agent and a first contact of the plurality of contacts as adjusted
in a first pair with a second difference in ordering between the
available agent and a second contact of the plurality of contacts
as adjusted different from the first contact in a second pair;
selecting, by the at least one computer processor, one of the first
pair and the second pair for connection based at least in part upon
the comparing; and outputting, by the at least one computer
processor, the selection of one of the first pair and the second
pair, wherein the selected one of the first pair and the second
pair is connected in the contact center system based at least in
part upon the outputting.
18. A system for hybrid behavioral pairing in a contact center
system comprising: at least one computer processor communicatively
coupled to and configured to operate in the contact center system,
wherein the at least one computer processor is further configured
to: determine an ordering of an available agent of a plurality of
agents in the contact center system; order a plurality of contacts;
adjust the ordering of the plurality of contacts to bias a first
strategy for pairing with a balanced contact utilization toward a
second strategy for pairing with a skewed contact utilization,
wherein an extent of the adjusting is based at least in part on a
hybridization function; compare, after the adjusting, a first
difference in ordering between the available agent and a first
contact of the plurality of contacts as adjusted in a first pair
with a second difference in ordering between the available agent
and a second contact of the plurality of contacts as adjusted
different from the first contact in a second pair; select one of
the first pair and the second pair for connection based at least in
part upon the comparing; and output the selection of one of the
first pair and the second pair, wherein the selected one of the
first pair and the second pair is connected in the contact center
system based at least in part upon the outputting.
19. The system of claim 18, wherein the at least one computer
processor is further configured to controllably target an
unbalanced agent utilization using the hybridization function.
20. The system of claim 19, wherein the at least one computer
processor is further configured to determine disproportionate
bandwidth for each of a plurality of contact types.
Description
FIELD OF THE DISCLOSURE
This disclosure generally relates to contact centers and, more
particularly, to techniques for hybrid behavioral pairing in a
contact center system.
BACKGROUND OF THE DISCLOSURE
A typical contact center algorithmically assigns contacts arriving
at the contact center to agents available to handle those contacts.
At times, the contact center may have agents available and waiting
for assignment to inbound or outbound contacts (e.g., telephone
calls, Internet chat sessions, email) or outbound contacts. At
other times, the contact center may have contacts waiting in one or
more queues for an agent to become available for assignment.
In some typical contact centers, contacts are assigned to agents
ordered based on time of arrival. This strategy may be referred to
as a "first-in, first-out", "FIFO", or "round-robin" strategy. In
some contact centers, contacts or agents are assigned into
different "skill groups" or "queues" prior to applying a FIFO
assignment strategy within each such skill group or queue. These
"skill queues" may also incorporate strategies for prioritizing
individual contacts or agents within a baseline FIFO ordering. For
example, a high-priority contact may be given a queue position
ahead of other contacts who arrived at an earlier time, or a
high-performing agent may be ordered ahead of other agents who have
been waiting longer for their next call. Regardless of such
variations in forming one or more queues of callers or one or more
orderings of available agents, contact centers typically apply FIFO
to the queues or other orderings. Once such a FIFO strategy has
been established, assignment of contacts to agents is automatic,
with the contact center assigning the first contact in the ordering
to the next available agent, or assigning the first agent in the
ordering to the next arriving contact. In the contact center
industry, the process of contact and agent distribution among skill
queues, prioritization and ordering within skill queues, and
subsequent FIFO assignment of contacts to agents is managed by a
system referred to as an "Automatic Call Distributor" ("ACD").
Some contact centers may use a "priority queuing" or "PQ" approach
to ordering the queue of waiting contacts. For example, the
ordering of contacts waiting for assignment to an agent would be
headed by the highest-priority waiting contact (e.g., the waiting
contact of a type that contributes to the highest sales conversion
rate, the highest customer satisfaction scores, the shortest
average handle time, the highest performing agent for the
particular contact profile, the highest customer retention rate,
the lowest customer retention cost, the highest rate of first-call
resolution). PQ ordering strategies attempt to maximize the
expected outcome of each contact-agent interaction but do so
typically without regard for utilizing contacts in a contact center
uniformly. Consequently, lower-priority contacts may experience
noticeably longer waiting times.
In view of the foregoing, it may be understood that there is a need
for a system that both attempts to utilize agents more evenly than
PQ while improving contact center performance beyond what FIFO
strategies deliver.
SUMMARY OF THE DISCLOSURE
Techniques for hybrid behavioral pairing in a contact center system
are disclosed. In one particular embodiment, the techniques may be
realized as a method for hybrid behavioral pairing in a contact
center system comprising: ordering an agent; ordering a plurality
of contacts; applying, by at least one processor, a hybridization
function to the ordering of the plurality of contacts to bias a
first strategy for pairing toward a second strategy for pairing;
comparing, by the at least one processor and based on the
hybridization function, a first difference in ordering between the
agent and a first contact in a first pair with a second difference
in ordering between the agent and a second contact different from
the first contact in a second pair; and selecting, by the at least
one processor, the first pair or the second pair for connection
based on the comparing.
In accordance with other aspects of this particular embodiment,
selecting the first pair or the second pair based on the comparing
may further comprise applying, by the at least one processor, a
diagonal strategy to the orderings.
In accordance with other aspects of this particular embodiment, the
ordering of the agent or the ordering of the plurality of contacts
may be expressed as percentiles.
In accordance with other aspects of this particular embodiment, the
ordering of the agent or the ordering of the plurality of contacts
may be expressed as percentile ranges.
In accordance with other aspects of this particular embodiment,
each of the plurality of contacts may be assigned a percentile
within each contact's respective percentile range.
In accordance with other aspects of this particular embodiment, an
assigned percentile may be a midpoint of a percentile range.
In accordance with other aspects of this particular embodiment, an
assigned percentile may be a random percentile of a percentile
range.
In accordance with other aspects of this particular embodiment, the
method may further comprise determining, by the at least one
processor, a bandwidth for the agent proportionate to a relative
performance of the agent.
In accordance with other aspects of this particular embodiment, the
hybridization function may enable controllably targeting, by the at
least one processor, an unbalanced contact utilization.
In accordance with other aspects of this particular embodiment,
applying the hybridization function may further comprise
determining, by the at least one processor, disproportional
bandwidth for each of a plurality of contact types.
In accordance with other aspects of this particular embodiment, a
selected contact or contact type of the selected pair may not be
any of: a contact or contact type lagging in a fairness metric, a
contact or contact type rated highest in a value, priority, or
performance metric, a contact or contact type rated highest in a
value, priority, or performance metric for a particular agent, a
contact previously assigned to the agent of the selected pair, a
sequentially labeled contact or contact type, or a randomly
selected contact or contact type.
In accordance with other aspects of this particular embodiment, the
selected one of the first pair and the second pair may comprise a
worse expected instant outcome than the other of the first pair and
the second pair.
In accordance with other aspects of this particular embodiment, a
higher-ordered agent may remain available for subsequent assignment
to a similarly higher-ordered contact, or a higher-ordered contact
may remain available for subsequent assignment to a similarly
higher-ordered agent.
In accordance with other aspects of this particular embodiment, the
first strategy may comprise a behavioral pairing strategy, and the
second strategy may comprise a priority queuing strategy.
In accordance with other aspects of this particular embodiment,
each successively higher-ordered contact of the plurality of
contacts may be more likely to be selected than respectively
lower-ordered contacts.
In accordance with other aspects of this particular embodiment,
each successively higher-ordered contact of the plurality of
contacts may comprise a lower average waiting time than
respectively lower-ordered contacts.
In another particular embodiment, the techniques may be realized as
a method for hybrid behavioral pairing in a contact center system
comprising: ordering, by at least one processor, an agent;
determining, by the at least one processor, a first ordering of a
plurality of contacts according to a first strategy for pairing;
determining, by the at least one processor, a second ordering of
the plurality of contacts according to a second strategy for
pairing; applying, by the at least one processor, a hybridization
function to combine the first ordering with the second ordering;
comparing, by the at least one processor and based on the
hybridization function, a first difference in ordering between the
agent and a first contact in a first pair with a second difference
in ordering between the agent and a second contact different from
the first contact in a second pair; and selecting, by the at least
one processor, the first pair or the second pair for connection
based on the comparing.
In another particular embodiment, the techniques may be realized as
a system for hybrid behavioral pairing in a contact center system
comprising: at least one processor, wherein the at least one
processor is configured to: order an agent; order a plurality of
contacts; apply a hybridization function to the ordering of the
plurality of contacts to bias a first strategy for pairing toward a
second strategy for pairing; compare, based on the hybridization
function, a first difference in ordering between the agent and a
first contact in a first pair with a second difference in ordering
between the agent and a second contact different from the first
contact in a second pair; and select the first pair or the second
pair for connection based on the comparing.
In accordance with other aspects of this particular embodiment, the
at least one processor may be further configured to controllably
target an unbalanced agent utilization using the hybridization
function.
In accordance with other aspects of this particular embodiment, the
at least one processor may be further configured to determine
disproportionate bandwidth for each of a plurality of contact
types.
The present disclosure will now be described in more detail with
reference to particular embodiments thereof as shown in the
accompanying drawings. While the present disclosure is described
below with reference to particular embodiments, it should be
understood that the present disclosure is not limited thereto.
Those of ordinary skill in the art having access to the teachings
herein will recognize additional implementations, modifications,
and embodiments, as well as other fields of use, which are within
the scope of the present disclosure as described herein, and with
respect to which the present disclosure may be of significant
utility.
BRIEF DESCRIPTION OF THE DRAWINGS
In order to facilitate a fuller understanding of the present
disclosure, reference is now made to the accompanying drawings, in
which like elements are referenced with like numerals. These
drawings should not be construed as limiting the present
disclosure, but are intended to be illustrative only.
FIG. 1 shows a block diagram of a contact center according to
embodiments of the present disclosure.
FIG. 2 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 3 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 4 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 5 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 6 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 7 shows a schematic representation of a queue according to
embodiments of the present disclosure.
FIG. 8 shows a flow diagram of a hybrid behavioral pairing method
according to embodiments of the present disclosure.
DETAILED DESCRIPTION
A typical contact center algorithmically assigns contacts arriving
at the contact center to agents available to handle those contacts.
At times, the contact center may be in an "L1 state" and have
agents available and waiting for assignment to inbound or outbound
contacts (e.g., telephone calls, Internet chat sessions, email). At
other times, the contact center may be in an "L2 state" and have
contacts waiting in one or more queues for an agent to become
available for assignment. Such L2 queues could be inbound,
outbound, or virtual queues. Contact center systems implement
various strategies for assigning contacts to agents in both L1 and
L2 states.
The present disclosure generally relates to contact center systems,
traditionally referred to as "Automated Call Distribution" ("ACD")
systems. Typically, such an ACD process is subsequent to an initial
"Skills-based Routing" ("SBR") process that serves to allocate
contacts and agents among skill queues within the contact center.
Such skill queues may distinguish contacts and agents based on
language capabilities, customer needs, or agent proficiency at a
particular set of tasks.
The most common traditional assignment method within a queue is
"First-In, First-Out" or "FIFO" assignment wherein the
longest-waiting contact is assigned to the longest-waiting agent.
Some contact centers implement "priority queuing" ("PQ") wherein
the next available agent is assigned to the highest-priority
contact. Variations of both such assignment methods commonly
exist.
Variations of FIFO typically target "fairness" inasmuch as they are
designed to balance the allocation ("utilization") of contacts to
agents over time. PQ variations of FIFO adopt a different approach
in which the allocation of contacts to agents is purposefully
skewed to increase the utilization of higher-priority contacts and
reduce the utilization of lower-priority contacts. PQ may do so
despite potential negative impacts on lower-priority contacts.
The present disclosure refers to optimized strategies for assigning
contacts to agents that improve upon traditional assignment
methods, such as "Behavioral Pairing" or "BP" strategies.
Behavioral Pairing targets balanced utilization of both agents and
contacts within queues (e.g., skill queues) while simultaneously
improving overall contact center performance potentially beyond
what FIFO or similar methods will achieve in practice. This is a
remarkable achievement inasmuch as BP acts on the same contacts and
same agents as FIFO, approximately balancing the utilization of
contacts as FIFO provides, while improving overall contact center
performance beyond what FIFO provides in practice.
BP improves performance by assigning agent and contact pairs in a
fashion that takes into consideration the assignment of potential
subsequent agent and contact pairs such that when the benefits of
all assignments are aggregated they may exceed those of FIFO and PQ
strategies. In some cases, BP results in instant contact and agent
pairings that may be the reverse of what FIFO or PQ would indicate.
For example, in an instant case BP might select the
shortest-waiting contact or the lowest-performing available agent.
BP respects "posterity" inasmuch as the system allocates contacts
to agents in a fashion that inherently forgoes what may be the
highest-performing selection at the instant moment if such a
decision increases the probability of better contact center
performance over time.
As explained in detail below, embodiments of the present disclosure
relate to techniques for "hybrid behavioral pairing" ("HBP"), which
combines strategies of BP with strategies of priority queuing, in a
manner in which a contact center administrator may adjust a balance
between the two. For example, a contact center administrator may
choose to have BP be the dominant mechanism for assigning contacts
from within a queue with a bias toward PQ. Instead of targeting a
balanced contact utilization, HBP may target a skewed contact
utilization. In some configurations, this bias or skew may be
slight; for example, an HBP strategy may be calibrated to reduce or
limit the number of occasions in which any one type of contact in a
queue (e.g., skill queue) is assigned to more than one agent
pairing before other types of contacts in the queue.
FIG. 1 shows a block diagram of a contact center system 100
according to embodiments of the present disclosure. The description
herein describes network elements, computers, and/or components of
a system and method for simulating contact center systems that may
include one or more modules. As used herein, the term "module" may
be understood to refer to computing software, firmware, hardware,
and/or various combinations thereof. Modules, however, are not to
be interpreted as software which is not implemented on hardware,
firmware, or recorded on a processor readable recordable storage
medium (i.e., modules are not software per se). It is noted that
the modules are exemplary. The modules may be combined, integrated,
separated, and/or duplicated to support various applications. Also,
a function described herein as being performed at a particular
module may be performed at one or more other modules and/or by one
or more other devices instead of or in addition to the function
performed at the particular module. Further, the modules may be
implemented across multiple devices and/or other components local
or remote to one another. Additionally, the modules may be moved
from one device and added to another device, and/or may be included
in both devices.
As shown in FIG. 1, the contact center system may include a central
switch 110. The central switch 110 may receive incoming contacts
(e.g., callers) or support outbound connections to contacts via a
dialer, a telecommunications network, or other modules (not shown).
The central switch 110 may include contact routing hardware and
software for helping to route contacts among one or more contact
centers, or to one or more PBX/ACDs or other queuing or switching
components within a contact center.
The central switch 110 may not be necessary if there is only one
contact center, or if there is only one PBX/ACD routing component,
in the contact center system 100. If more than one contact center
is part of the contact center system 100, each contact center may
include at least one contact center switch (e.g., contact center
switches 120A and 120B). The contact center switches 120A and 120B
may be communicatively coupled to the central switch 110.
Each contact center switch for each contact center may be
communicatively coupled to a plurality (or "pool") of agents. Each
contact center switch may support a certain number of agents (or
"seats") to be logged in at one time. At any given time, a
logged-in agent may be available and waiting to be connected to a
contact, or the logged-in agent may be unavailable for any of a
number of reasons, such as being connected to another contact,
performing certain post-call functions such as logging information
about the call, or taking a break.
In the example of FIG. 1, the central switch 110 routes contacts to
one of two contact centers via contact center switch 120A and
contact center switch 120B, respectively. Each of the contact
center switches 120A and 120B are shown with two agents each.
Agents 130A and 130B may be logged into contact center switch 120A,
and agents 130C and 130D may be logged into contact center switch
120B.
The contact center system 100 may also be communicatively coupled
to an integrated service from, for example, a third party vendor.
In the example of FIG. 1, hybrid behavioral pairing module 140 may
be communicatively coupled to one or more switches in the switch
system of the contact center system 100, such as central switch
110, contact center switch 120A, or contact center switch 120B. In
some embodiments, switches of the contact center system 100 may be
communicatively coupled to multiple hybrid behavioral pairing
modules. In some embodiments, hybrid behavioral pairing module 140
may be embedded within a component of a contact center system
(e.g., embedded in or otherwise integrated with a switch).
The hybrid behavioral pairing module 140 may receive information
from a switch (e.g., contact center switch 120A) about agents
logged into the switch (e.g., agents 130A and 130B) and about
incoming contacts via another switch (e.g., central switch 110) or,
in some embodiments, from a network (e.g., the Internet or a
telecommunications network) (not shown).
The hybrid behavioral pairing module 140 may process this
information and to determine which contacts should be paired (e.g.,
matched, assigned, distributed, routed) with which agents. For
example, multiple agents are available and waiting for connection
to a contact (L1 state), and a contact arrives at the contact
center via a network or central switch. As explained below, without
the hybrid behavioral pairing module 140 or similar behavioral
pairing module, a contact center switch will typically
automatically distribute the new contact to whichever available
agent has been waiting the longest amount of time for an agent
under a "fair" FIFO strategy, or whichever available agent has been
determined to be the highest-performing agent under another
strategy such as a performance-based routing ("PBR") strategy.
With the hybrid behavioral pairing module 140 or a similar
behavioral pairing module, contacts and agents may be given scores
(e.g., percentiles or percentile ranges/bandwidths) according to a
pairing model or other artificial intelligence data model, so that
a contact may be matched, paired, or otherwise connected to a
preferred agent. In some embodiments, the hybrid behavioral pairing
module 140 may be configured with an HBP strategy that blends the
BP and PBR strategies, targeting biased rather than balanced agent
utilization.
In an L2 state, multiple contacts are available and waiting for
connection to an agent, and an agent becomes available. These
contacts may be queued in a contact center switch such as a PBX or
ACD device ("PBX/ACD"). Without the hybrid behavioral pairing
module 140 or a similar behavioral pairing module, a contact center
switch will typically connect the newly available agent to
whichever contact has been waiting on hold in the queue for the
longest amount of time as in a "fair" FIFO strategy or a PBR
strategy when agent choice is not available. In some contact
centers, priority queuing may also be incorporated.
With the hybrid behavioral pairing module 140 or similar behavioral
pairing module in an L2 scenario, as in the L1 state described
above, contacts and agents may be given percentiles (or percentile
ranges/bandwidths, etc.) according to, for example, a model, such
as an other artificial intelligence model, so that an agent coming
available may be matched, paired, or otherwise connected to a
preferred contact.
Under an HBP strategy, a hybridization factor or function may be
applied to one or more orderings of agents to achieve the desired
balance between a BP strategy, which targets a balanced
utilization, and a PQ strategy, which targets a highly skewed
utilization during periods of time when a contact center is in an
L2 state (i.e., multiple contacts waiting for assignment).
In some embodiments, a hybridization function may combine two (or
more) orderings or other types of ranking systems together. For
example, a contact center may have four contacts of different
types: Contact A, Contact B, Contact C, and Contact D ("A", "B",
"C", and "D") available for pairing with an agent. The contacts may
be ordered according to multiple ordering systems. For example,
under a typical FIFO strategy, the agents may be ordered according
to how long each contact has been waiting for an assignment
relative to the other contacts. Under a typical priority queuing
strategy, the contacts may be ordered according to how well each
contact contributes to performance for some metric relative to the
other contacts. Under a BP strategy, the agents may be ordered
according to the quality of each agent's "behavioral fit" relative
to the other agents.
One technique for combining two orderings is to determine a sum.
For example, if a PQ strategy orders the four contacts as A=1, B=2,
C=3, and D=4, the PQ strategy would preferably pair
highest-"performing" Contact A with the next agent. And if a BP
strategy order the contacts as A=4, B=2, C=3, D=1, the BP strategy
would preferably pair best-fitting Contact D with the next agent.
In this example of an HBP strategy, the sum of the two orderings
would be A=5, B=4, C=6, D=5. This HBP strategy would preferably
pair Contact B with the next agent, which is the second
highest-performing and second best-fitting agent according to the
original orderings.
Other embodiments may use other techniques for combining multiple
orderings of agents. For example, the HBP ordering may be a product
obtained by multiplying two or more orderings. For another example,
the HBP ordering may be a weighted sum or product obtained by
scaling the one or more of the orderings by a scaling factor. In
this way, HBP may be configured to weight an agent's relative
performance more or less than the agent's relative behavioral
fit.
FIG. 2 shows a queue 200 according to embodiments of the present
disclosure operating under BP Strategy 210. Queue 200 represents a
simplified hypothetical case in which four types of contacts may be
assigned to any of four agents in an environment in which the
contact center is seeking to maximize a desired metric (e.g.,
sales). The four evenly distributed types of contacts are assigned
percentile ranges (or "bandwidths") of 0.00 to 0.25 ("0-25%
Contacts"), 0.25 to 0.50 ("25-50% Contacts"), 0.50 to 0.75 ("50-75%
Contacts"), and 0.75 to 1.00 (75-100% Contacts). The four agents
occupy equally-spaced percentile bandwidths and are assigned
percentiles at the midpoints of their respective ranges: 0.00 to
0.25 ("0.125 Agent"), 0.25 to 0.50 ("0.375 Agent"), 0.50 to 0.75
("0.625 Agent"), and 0.75 to 1.00 ("0.875 Agent"). The four agents
may also be ordered by performance according to a desired metric
(e.g., sales), such that the lowest-performing agent is assigned
the lowest percentile (the 0.125 Agent), and the highest-performing
agent is assigned the highest percentile (the 0.875 Agent).
By applying a diagonal strategy, 0-25% Contacts may be preferably
assigned to the 0.125 Agent, 25-50% Contacts may be preferably
assigned to the 0.375 Agent, 50-75% Contacts may be preferably
assigned to the 0.625 Agent, and 75-100% Contacts may be preferably
assigned to the 0.875 Agent. BP Strategy 210 targets a balanced
utilization, with each agent receiving approximately the same
proportion of contacts over time. Accordingly, there is no bias
toward a PQ strategy, under which contact utilization would be
skewed toward utilizing the highest-performing 75-100% Contacts
more heavily.
One such technique for generating the performance-biased contact
type percentiles according to embodiments of the present disclosure
is to adjust each contact type's "initial" midpoint percentile
("CP.sub.initial") by a hybridization function or factor, such that
relatively higher-ordered (e.g., higher-performing) contacts occupy
relatively larger bandwidths and, consequently, receive relatively
more contacts than lower-ordered (e.g., lower-performing) contacts.
For example, the hybridization function may raise each contact's
percentile to a power, as in Equation 1 below:
CP.sub.adjusted=CP.sub.initial.sup..rho. (Eqn. 1) The power
parameter (e.g., ".rho." or a "Rho parameter" as in Equation 1 may
determine the amount of bias toward PQ, with higher values of Rho
generating greater amounts of bias. A Rho parameter of 1.0 would
generate no bias (CP.sub.adjusted=CP.sub.initial). Thus, this
"neutral" value for Rho results in targeting a balanced contact
utilization. In fact, BP Strategy 210 is equivalent to a Rho-based
HBP strategy in which Rho equals 1.0. As Rho increases, the degree
of contact utilization skew increases as bias toward PQ
increases.
FIG. 3 shows a queue 300 that applies this technique using a Rho
value of 2.0. Queue 300 represents the same four types of contacts
and the same four agents as in queue 200. However, in queue 300,
the contact types' percentile midpoints have been squared
(CP.sub.adjusted=CP.sub.initial.sup.2.0). Applying a diagonal
strategy under HBP Strategy 310, the lowest-ordered contact type
(CP.sub.adjusted.apprxeq.0.016) would occupy the smallest bandwidth
and be selected least frequently, and so on, up to the
highest-ordered contact type (CP.sub.adjusted.apprxeq.0.766), which
would occupy the largest bandwidth and be selected most
frequently.
In some embodiments, the bandwidth of each contact type may be
determined so that each contact type's adjusted percentile midpoint
is the midpoint of each contact type's new, adjusted bandwidth. For
example, the bandwidth of the lowest-ordered 0.016 contact type may
be approximately 0.000 to 0.031 In other embodiments, the bandwidth
of each agent may be determined by equally distributing the
"distance" between neighboring adjusted percentile midpoints. For
example, the bandwidth of the lowest-ordered 0.016 contact type may
be approximately 0.000 to 0.079.
Another variation of the HBP technique applied to queue 300 in FIG.
3 is to adjust each contact type's initial percentile ranges rather
than each contact type's initial midpoint percentile, as in
Equation 2 below:
CP.sub.adjusted.sub._.sub.range=CP.sub.initial.sub._.sub.range.sup..rho.
(Eqn. 2) The effect would be the same: relatively higher-ordered
(e.g., higher value) contact types occupy relatively larger
bandwidths and, consequently, are selected relatively more
frequently than lower-ordered (e.g., lower value) contact
types.
FIG. 4 shows a queue 400 that applies this technique using a Rho
value of 2.0. Queue 400 represents the same four contact types and
agents as in queue 300. However, in queue 400, the contact types'
initial percentile ranges have been squared
(CP.sub.adjusted.sub._.sub.range=CP.sub.initial.sub._.sub.range.sup.2.0
instead of their initial midpoint percentiles. Applying a diagonal
strategy under HBP Strategy 410, the lowest-ordered contact type
(occupying adjusted percentile range from 0.00 to approximately
0.06 with a midpoint of approximately 0.03) would be selected least
frequently, and so on, up to the highest-ordered contact type
(occupying adjusted percentile range from approximately 0.56 to
1.00 with a midpoint of approximately 0.82), which would be
selected most frequently.
Conceptually, the target skewed utilization would result in the
highest-ordered contact type being selected a little less than half
of the time, typically when one of the top-half of agents becomes
available, and the lower-ordered contact types being selected a
little more than half of the time, typically when one of the
bottom-half of agents becomes available. Other techniques for
visualizing or implementing these hybridization functions or
factors include adjusting the "fitting function" of the diagonal
strategy.
FIG. 5 shows a queue 500 with the same contact type percentiles and
ranges as in queue 100 (FIG. 1), and they have not been adjusted.
Unlike queue 100, in which BP Strategy 110 may be visualized by a
45-degree diagonal line (CP=AP), HBP strategy 510 may be visualized
by a different, hybridized fitting function (e.g., "bending" or
"bowing" the diagonal line). In the example, of FIG. 5, the fitting
function is an exponential function, as in Equation 3 below:
AP=CP.sup..rho. (Eqn. 3) Conceptually, instead of determining
preferred pairings by selecting pairs closest to the diagonal AP=CP
as in BP Strategy 110, preferred pairings in HBP Strategy 510 may
be determined by selecting pairs closest to the exponential
AP=CP.sup.2.0 as in queue 500 where Rho equals 2.0. Notably, the
effect of fitting to AP=CP.sup.2.0 is the continuous mathematical
analogue to the discontinuous process of broadening or shrinking
percentile ranges (e.g., squaring the percentile ranges and then
fitting to AP=CP, as in queue 400 and HBP Strategy 410 (FIG.
4).
Many variations of hybridization functions may be used to vary the
target utilization of an agent as a function of the agent's
performance or other ordering or metric. For example, a
hybridization function may be a piecewise function.
FIG. 6 shows a queue 600 and HBP Strategy 610 that affects the
utilization of the bottom-half of the contact types differently
than that of the top-half of the contact types. For example, the
contact center may determine that half of the contacts should be
distributed to below-average agents in a balanced manner (e.g.,
Rho=1.0), but the other half of the contacts should be distributed
to above-average agents according to each contact type's relative
ordering (e.g., Rho>1.0). Thus, contacts ranging from 0% to 50%
may be distributed to the lower-performing agents (0.125 Agent and
0.375 Agent) evenly, visualized as a fit along the 45-degree line
AP=CP for 0.00.ltoreq.CP<0.50 (or, e.g., 0.00<CP.ltoreq.0.50,
etc.). Contacts ranging from 50% to 100% may be distributed to the
higher-performing agents (0.625 Agent and 0.875 Agent) as a
function of their contact type's relative ordering (e.g., value),
such as an exponential function scaled to this portion of contacts
and agents. HBP Strategy 610 may be visualized as a fit along the
exponential curve AP=2(CP-0.5).sup.2.0+0.5 for Rho=2.0 and
0.50.ltoreq.CP<1.00.
Incidentally, such a strategy would result in some higher-ordered
contact types (here, the 0.50 to 0.75 contact type) being selected
less frequently over time than its lower-ordered peers. FIG. 7
shows a queue 700 and HBP Strategy 710 that also affects the
utilization of the bottom-half of the contacts differently than
that of the top-half of the contacts using a piecewise
hybridization function. For example, the contact center may
determine that a larger portion of contacts should be distributed
to above-average agents according to their relative ordering (e.g.,
Rho>1.0), and the remaining portion of contacts should be
distributed to below-average agents in a balanced manner (e.g.,
Rho=1.0). Thus, for Rho=2.0 and CP.gtoreq.0.50 (or CP>0.50),
pairings may be fit along the exponential curve AP=CP.sup.2.0. For
Rho=1.0 and CP<0.50, pairings may be fit along a linear
function, scaled to this portion of contacts and agents:
AP=0.5CP.
In real-world contact centers, there may be more or fewer agents,
and more or fewer contact types in a queue. In these examples, each
contact type is evenly distributed within the total range of
percentile ranks; however, in some contact centers, the
distribution of ranges could vary based on, for example, the
frequency at which contacts of a particular type arrive at a
contact center relative to the frequency at which contacts of other
types arrive. The simplified examples described above, with four
agents and four contact types, are used to illustrate the effects
of an implicit form of HBP such as those based on a Rho parameter
and exponential scaling or other hybridization functions. However,
HBP--including Rho-based techniques--may also be applied to bigger,
more complex, real-world contact centers.
In some embodiments, Rho may be selected or adjusted to vary the
bias toward PQ (or skew in contact utilization). For example, Rho
less than 2.0 (e.g., 1.0, 1.01, 1.1, 1.2, 1.5, etc.) would result
in relatively less bias toward PQ than the examples above in which
Rho equals 2.0. For example, if a contact center administrator
wanted to avoid occurrences of higher-ordered contact types being
selected multiple times while a lower-ordered contact type remains
unselected, a significantly lower value of Rho may be more
appropriate than 2.0. Conversely, Rho greater than 2.0 (e.g., 2.01,
2.1, 2.5, 200.0, etc.) would result in relatively more bias toward
PQ.
Importantly, the effect on contact utilization is subtle under
Rho-based HBP strategies inasmuch as they controllably affect the
degree to which contacts of differently ordered contact types wait
for connection to an agent. By increasing the power to which
contact percentiles are raised, this invention controllably
decreases the average time between selections for higher-ordered
contact types and increases the average time between selections for
comparatively lower-ordered contact types. Similarly, reducing the
power to which contact percentiles are raised has the reverse
effect. For neutral BP strategies (e.g., Rho=1.0), each agent has
approximately the same expected average waiting time between
contacts. As Rho increases, the relative expected average waiting
time progressively (e.g., exponentially) decreases as relative
contact type ordering (e.g., contact type value) increases.
In some embodiments, an HBP strategy may target relative contact
utilization using potentially more gradual techniques. For example,
contact types may be assigned relative "utilization adjustments"
based on contact type ordering. In one example, the highest-ordered
contact type may be assigned a relative utilization adjustment of
100%, the second-highest contact type a relative utilization of
99%, the third 98%, and so on. In this example, the target
utilization of the second-highest ordered contact type would be 99%
of the target utilization of the highest-ordered contact type. The
relative utilization adjustment may be more aggressive in other
configurations. For example, the highest-ordered contact type may
be assigned a relative utilization of 100%, the second-highest
contact type 90%, the third 80%, and so on. In this example, the
target utilization of the second-highest ordered contact type would
be 90% of the target utilization of the highest-ordered contact
type.
FIG. 8 shows a hybrid behavioral pairing method 800 according to
embodiments of the present disclosure. At block 810, hybrid
behavioral pairing method 800 may begin.
At block 810, a percentile (or n-tile, quantile, percentile range,
bandwidth, or other type of "score" or range of scores, etc.) may
be determined for each available contact. For situations in which
contacts are waiting on hold in a queue, percentiles may be
determined for each of the contacts waiting on hold in the queue.
For situations in which contacts are not waiting on hold in a
queue, a percentile may be assigned to the next contact to arrive
at the contact center. The percentiles may be bounded by a range of
percentiles defined for a particular type or group of contacts
based on information about the contact. The percentile bounds or
ranges may be based on a frequency distribution or other metric for
the contact types. The percentile may be randomly assigned within
the type's percentile range.
In some embodiments, percentiles may be ordered according to a
particular metric or combination of metrics to be optimized in the
contact center, and a contact determined to have a relatively high
percentile may be considered to be a "higher-value" contact for the
contact center inasmuch as these contacts are more likely to
contribute to a higher overall performance in the contact center.
For example, a relatively high-percentile contact may have a
relatively high likelihood of making a purchase.
In some embodiments, a percentile may be determined for a contact
at the time the contact arrives at the contact center. In other
embodiments, a percentile may be determined for the contact at a
later point in time, such as when the contact arrives at a
particular skill queue or ACD system, or when a request for a
pairing is made.
After a percentile has been determined for each contact available
for pairing, behavioral pairing method 800 may proceed to block
820. In some embodiments, block 820 may be performed prior to, or
simultaneously with, block 810.
At block 820, a percentile may be determined for each available
agent. For situations in which agents are idle, waiting for
contacts to arrive, percentiles may be determined for each of the
idle agents. For situations in which agents for a queue are all
busy, a percentile may be determined to the next agent to become
available. The percentiles may be bounded by a range of percentiles
(e.g., "bandwidth") defined based on all of agents assigned to a
queue (e.g., a skill queue) or only the available agents assigned
to a particular queue. In some embodiments, the bounds or ranges of
percentiles may be based on a desired agent utilization (e.g., for
fairness, efficiency, or performance).
In some embodiments, agent percentiles may be ordered according to
a particular metric or combination of metrics to be optimized in
the contact center, and an agent determined to have a relatively
high percentile may be considered to be a higher-performing agent
for the contact center. For example, a relatively high-percentile
agent may have a relatively high likelihood of making a sale.
In some embodiments, an agent's percentile may be determined at the
time the agent becomes available within the contact center. In
other embodiments, a percentile may be determined at a later point
in time, such as when a request for a pairing is made.
After a percentile has been determined for each available agent and
contact, behavioral pairing method 800 may proceed to block
830.
At block 830, a hybridization function may be applied to contact
type percentiles (or contact type percentile ranges or bandwidths).
For example, a Rho value may be determined for an exponential
hybridization function or fitting curve or line. In some
embodiments, the hybridization function may act on a single
ordering that implicitly incorporates both behavioral fit and
performance information. In other embodiments, the hybridization
function may combine (e.g., add, multiply, weight) multiple
orderings of contact types. After the hybridization function has
been applied or otherwise determined or configured, hybrid
behavioral pairing method 800 may proceed to block 840.
At block 840, a pair of an available contact and an available agent
may be determined based on the percentiles (or percentile ranges)
determined for each available contact at block 810 and for each
available agent at block 820 based on a hybridization function. In
some embodiments, the selection may be determined based on
percentiles or percentile ranges for each waiting contact or
contact type adjusted at block 830. In some embodiments, the pair
may be determined according to a diagonal strategy, in which
contacts and agents with more similar percentiles (or the most
similar percentiles) may be selected for pairing. For example, a
hybrid behavioral pairing module may select a contact-agent pairing
with the smallest absolute difference between the contact's score
and the agent's score. In some embodiments, the diagonal strategy
may be visualized as a 45-degree diagonal line. In other
embodiments, the diagonal strategy may be visualized as a
hybridization function (e.g., an exponential function, or a
piecewise function).
In some situations, multiple agents may be idle when a contact
arrives (an L1 state). Under HBP, the newly available contact may
be paired with a selected one of the available agents that has a
percentile or percentile range more similar to the contact's
adjusted percentile than other available agents. In other
situations, multiple contacts may be waiting in a queue when an
agent becomes available (an L2 state). Under HBP, the newly
available agent may be paired with a selected one of the contacts
waiting in the queue that has an adjusted percentile more similar
to the agent's percentile or percentile range than other contacts
waiting in the queue.
In some situations, selecting a pairing based on similarity of
scores may result in selecting an instant pairing that might not be
the highest performing instant pairing, but rather increases the
likelihood of better future pairings.
After a pairing has been determined at block 840, hybrid behavioral
pairing method 800 may proceed to block 850. At block 850, modules
within the contact center system may cause the contact and agent of
the contact-agent pair to be connected with one another. For
example, a behavioral pairing module may indicate that an ACD
system or other routing device may distribute a particular contact
to a particular agent.
After connecting the contact and agent at block 850, behavioral
pairing method 800 may end. In some embodiments, behavioral pairing
method 800 may return to block 840 for determining one or more
additional pairings (not shown). In other embodiments, behavioral
pairing method 800 may return to block 810 or block 820 to
determine (or re-determine) percentiles or percentile ranges for
available contacts or agents (not shown), and subsequently apply
(or reapply) a hybridization function at block 840.
At this point it should be noted that hybrid behavioral pairing in
a contact center system in accordance with the present disclosure
as described above may involve the processing of input data and the
generation of output data to some extent. This input data
processing and output data generation may be implemented in
hardware or software. For example, specific electronic components
may be employed in a behavioral pairing module or similar or
related circuitry for implementing the functions associated with
behavioral pairing in a contact center system in accordance with
the present disclosure as described above. Alternatively, one or
more processors operating in accordance with instructions may
implement the functions associated with behavioral pairing in a
contact center system in accordance with the present disclosure as
described above. If such is the case, it is within the scope of the
present disclosure that such instructions may be stored on one or
more non-transitory processor readable storage media (e.g., a
magnetic disk or other storage medium), or transmitted to one or
more processors via one or more signals embodied in one or more
carrier waves.
The present disclosure is not to be limited in scope by the
specific embodiments described herein. Indeed, other various
embodiments of and modifications to the present disclosure, in
addition to those described herein, will be apparent to those of
ordinary skill in the art from the foregoing description and
accompanying drawings. Thus, such other embodiments and
modifications are intended to fall within the scope of the present
disclosure. Further, although the present disclosure has been
described herein in the context of at least one particular
implementation in at least one particular environment for at least
one particular purpose, those of ordinary skill in the art will
recognize that its usefulness is not limited thereto and that the
present disclosure may be beneficially implemented in any number of
environments for any number of purposes. Accordingly, the claims
set forth below should be construed in view of the full breadth and
spirit of the present disclosure as described herein.
* * * * *
References